We employed another dataset: Beijing Air Quality (BAQ) [1]}.
All BAQ time-series data consist of 10 time series contain to air quality, \(SO_2\) , and more.
Objects with time lengths of 35,065, the size of fine dust, are contained in the set, including temperature, humidity, etc., taken from monitoring sites near Beiging.
According to Table REF , NAM-NC\(_{feature}\)  performed the best in most metrics among NAM-NCs. It is seemed that NAM-NCs with shared parameters provide computational efficiency, but do not degraded the prediction performance. SCINet showed the best prediction performance for most datasets; however the NAM-NCs showed comparable results while providing the explanation power.
Figure REF  shows the contributions generated by the feature net of NAM-NC trained four times to repeat each NC target of BAQ.
According to the figure, NAM-NC captured the time series which is correlated, as temperature, pressure, and dew point. Also, we explained the NAM-NC's prediction for the \(NO_2\)  and \(O_3\)  that received the contribution from the distant past, such as pressure at \(t=-8\) .
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